Welcome to the Applied Math and Computer Science Laboratory (AML-CS) at Universidad del Norte in Barranquilla, Colombia. The group was founded in Apr, 2017. AML-CS is a space that brings together people from different fields of science. We are highly motivated to solve real-life problems via scientific computing, mathematics, and statistics. Our students have the chance to face issues such as those found in Data Assimilation, Inverse Problems, Applied Statistics, and Numerical Optimization. For instance, Data-Driven models are of primary interest for us; it is fascinating to see what data can tell us about the underlying (physical) process. In this manner, we can forecast based on our statistical knowledge of the process. Also, we employ numerical models to predict physical phenomena and to understand the world where we live. In general, we are open to solve problems in the following fields:

  • Data Assimilation Methods.

  • Inverse Problems and Parameter Estimation.

  • Combinatorial Optimization.

  • Numerical Optimization.

  • Bayesian Inference.

  • High Performance Computing

Feel free to contact me if you want to be part of my group.

Elias D. Nino-Ruiz, Director. (aml-cs@uninorte.edu.co)

Director:

Elias D. Nino-Ruiz, Ph.D.

Open positions for M.Sc. students: I have three (3) open positions for M.Sc. students to start in Spring 2020 at Universidad del Norte. The candidates should have good skills in Mathematics and Statistics. Research topics:

  • Data-Driven models for Weather Forecasts + Data Assimilation.

  • 4D-Var Data Assimilation Methods in High-Performance-Computing (HPC)

Your tuition fees will be covered. You will receive a stipend as well.

Candidates can submit their short CVs to enino at uninorte dot edu dot co

Carousel image
Worldwide temperatures levels at surface

Error bounds for solving linear systems in the preconditioned Crank-Nicolson scheme. The black line denotes the boundary between convergence and divergence in proposal steps. Nino-Ruiz, E. D. A numerical method for solving linear systems in the preconditioned Crank–Nicolson algorithm. Applied Mathematics Letters, Eslevier. (2020). https://doi.org/10.1016/j.aml.2020.106254.

Relevant Courses for Students in this Lab:

  • Data Mining.

  • Data Assimilation.

  • Theory of Optimization.

  • Variational Data Assimilation.

  • Bayesian Inference.

  • Finite Differences.

  • Finite Elements.

Talks by Dr. Elias Nino-Ruiz:

  1. 15/05/2020 - Métodos de Machine Learning e Inteligencia Artificial: Oportunidades Para Estimar el Impacto del SAR-COV-2 en Colombia - (Video) (Spanish)

  2. 19/09/2019 - Efficient Implementation of Ensemble Based Methods - First International Workshop on Data Assimilation for Decision Making, Barranquilla, Colombia (ENGLISH). (Video)

  3. 22/01/2019 - Ensemble Kalman Filter Based On A Modified Cholseky Decomposition, ISDA 2019 - 7th International Symposium on Data Assimilation, RIKEN R-CCS, Kobe, Japan (ENGLISH). (Video)

  4. 27/11/2018 - Covariance Matrix Estimation - Seminar of the Ph.D. in Mathematical Engineering, Universidad EAFIT, Colombia (SPANISH). (Video)

Talk Series in Computer Science and Applications

  1. (31/07/2020) - (Spanish) - Juan Carlos De los Reyes, Ph.D., Algunos Aspectos Teóricos y Prácticos de la Asimilación de Datos en la Predicción Meteorológica. Centro de Modelización Matemática (MODEMAT), Escuela Politécnica Nacional, Ecuador. (VIDEO)

  2. (14/07/2020) - (English) - Haiyan Cheng, Ph.D.

Current Students

  • Randy Steven Consuegra Ortega, M.Sc. in Computer Science. Data-Driven Models for Variational Data Assimilation. Website

  • Juan Sebastian Rodriguez Donado, M.Sc. in Computer Science. Data Assimilation Method for Air Quality Estimation. Website

  • Andres Felipe Movilla Obregon, M.Sc. in Computer Science. Website

  • Omar Angel Mejia Suarez, M.Sc. in Computer Science. Data Assimilation Method for Air Quality Estimation. Website


  • Alfonso Manuel Mancilla Herrera, Ph,D. in Computer Science, Non-Linear Data Assimilation Methods.

  • Felipe Jose Acevedo Garcia, M.Sc. in Computer Science. Website


Former Students

  • Juan C. Calabria Sarmiento, Ph.D. in Computer Science, Data Assimilation Methods for Wind Energy Potential Estimation. (2020).

  • Luis Gabriel Guzman Reyes, Ph.D. in Computer Science, Shrinkage Covariance Matrix Estimation in Ensemble-Based Data Assimilation. (2020). Website

  • Rolando Beltran Arrieta, Ph.D. in Computer Science, Non-Linear Data Assimilation via Sampling Methods. (2019).

  • Luis Ernesto Morales Retat, M.Sc. in Computer Science, Soft Computing Methods for Optimal Radius of Influence Estimation in Ensemble Based Data Assimilation. (2018).

Awards:

  1. Best Workshop Paper Award. A Surrogate Model Based On Mixtures Of Taylor Expansions For Trust Region Based Methods. ICCS 2017, Zurich, Zwitserland, June 2017. More info: http://www.iccs-meeting.org/iccs2017/awards/

Publications


  1. Nino-Ruiz, E. D. (2020, June). A Random Line-Search Optimization Method via Modified Cholesky Decomposition for Non-linear Data Assimilation. In International Conference on Computational Science (pp. 189-202). Springer, Cham. - ( HTML )

  2. Montoya, O.L.Q., Niño-Ruiz, E.D. & Pinel, N. On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes. Environ Sci Pollut Res, Springer. (2020). https://doi.org/10.1007/s11356-020-08268-4

  3. Nino-Ruiz, E.D., Mancilla-Herrera, A., Lopez-Restrepo, S., and Quintero-Montoya, O. A Maximum Likelihood Ensemble Filter Via A Modified Cholesky Decomposition For Non-Gaussian Data Assimilation. Sensors, MPDI. (2020). https://doi.org/10.3390/s20030877

  4. Elias D. Nino-Ruiz, Juan C. Calabria-Sarmiento, Luis G. Guzman-Reyes, and Alvin Henao. A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation. Atmosphere, MPDI. (2020). https://doi.org/10.3390/atmos11020167

  5. Nino-Ruiz, E. D. A numerical method for solving linear systems in the preconditioned Crank–Nicolson algorithm. Applied Mathematics Letters, Eslevier. (2020). https://doi.org/10.1016/j.aml.2020.106254.

  6. Elias D. Nino-Ruiz, Rolando Beltran-Arrieta, & Luis Guzman-Reyes. An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula. Non-Linear Dynamics, Springer. (2019). https://doi.org/10.1007/s11071-019-05411-w

  7. Jairo Pimentel, Carlos Julio Ardila Hernandez, Elías Niño, Daladier Jabba Molinares, Jonathan Ruiz-Rangel. Water Cycle Algorithm: Implementation and Analysis of Solutions to the Bi-Objective Travelling Salesman Problem, International Journal of Artificial Intelligence, CESER, Volume 17 (2), (2019). http://www.ceser.in/ceserp/index.php/ijai/article/view/6256

  8. Elias D. Nino-Ruiz, Xin-She Yang, Improved Tabu Search and Simulated Annealing methods for nonlinear data assimilation, Applied Soft Computing, Elsevier, Volume 83, (2019). https://doi.org/10.1016/j.asoc.2019.105624

  9. Nino-Ruiz, E. D. Non-linear data assimilation via trust region optimization. Computational and Applied Mathematics, Springer, 38:129 (2019). https://doi.org/10.1007/s40314-019-0901-x

  10. Elias D. Nino-Ruiz, Carlos Ardila, Jesus Estrada and Jose Capacho. A reduced-space line-search method for unconstrained optimization via random descent directions. Applied Mathematics and Computation, Elsevier, 341(2018): 15-30. https://doi.org/10.1016/j.amc.2018.08.020

  11. Nino-Ruiz, E. D., Mancilla-Herrera, A. M., & Beltran-Arrieta, R. (2018, May). Non-Gaussian data assimilation via modified cholesky decomposition. In 2018 7th International Conference on Computers Communications and Control (ICCCC) (pp. 29-36). IEEE. https://ieeexplore.ieee.org/document/8390433/

  12. Elias D. Nino-Ruiz & Luis E. Morales-Retat. A Tabu Search implementation for adaptive localization in ensemble-based methods. Soft Computing, Springer (2018) https://doi.org/10.1007/s00500-018-3210-1

  13. Nino-Ruiz, Elias D.; Cheng, Haiyan; Beltran, Rolando. A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models. Atmosphere 9, no. 4: 126. (2018), http://www.mdpi.com/2073-4433/9/4/126

  14. Elias D. Nino-Ruiz. Implicit Surrogate Models For Trust Region Based Methods, Journal of Scientific Computing, Elsevier, (2018), https://doi.org/10.1016/j.jocs.2018.02.003

  15. Elias D. Nino-Ruiz, A Matrix-Free Posterior Ensemble Kalman Filter Implementation Based on a Modified Cholesky Decomposition. Atmosphere Journal, 8:125, (2017), https://doi.org/10.3390/atmos8070125

  16. Nino-Ruiz, E.D., Ardila, C. & Capacho, R. Local search methods for the solution of implicit inverse problems, Soft Computing, Springer (2017), https://doi.org/10.1007/s00500-017-2670-z

  17. Elias D. Nino-Ruiz, Carlos J. Ardila, Alfonso Mancilla, Jesus Estrada, A Surrogate Model Based On Mixtures Of Taylor Expansions For Trust Region Based Methods. Procedia Computer Science, Volume 108, 2017, Pages 1473-1482, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2017.05.200.

  18. Elias D. Nino-Ruiz, Alfonso Mancilla, Juan C. Calabria, A Posterior Ensemble Kalman Filter Based On A Modified Cholesky Decomposition, Procedia Computer Science, Volume 108, 2017, Pages 2049-2058, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2017.05.062.